“…The success of microbiome studies (composition, structure, diversity, and function) is primarily ascribable to the development of bioinformatics tools embedded in creative algorithms specially tailored to overcome the technical challenges posed by the analysis of massively paralleled, high-throughput sequencing data ( Simon and Daniel, 2011 ; Siegwald et al, 2017 ). These bioinformatics tools make use of several techniques (e.g., read mapping, k-mer alignment, and composition analysis) ( Piro et al, 2017 ) and can be categorized into two distinct groups: (1) programs that use all available genome sequences ( Lindgreen et al, 2016 ), also called assignment-first approaches ( Siegwald et al, 2017 ) (e.g., CLARK – Ounit et al, 2015 ; GOTTCHA – Freitas et al, 2015 ; KRAKEN – Wood and Salzberg, 2014 ; MG-RAST – Meyer et al, 2008 ), and (2) programs that target a set of marker genes ( Lindgreen et al, 2016 ), also known as clustering-first approaches ( Siegwald et al, 2017 ) (e.g., QIIME – Caporaso et al, 2010 ; MOTHUR – Schloss et al, 2009 ; MetaPhlAn – Segata et al, 2012 ; mOTU – Sunagawa et al, 2013 ). In the assignment-first tools, all reads are assigned to the lowest taxonomy unit (lower common ancestor-LCA) within a reference database based on their annotations, while in the clustering-first approaches the reads are grouped into Operational Taxonomic Units (OTUs) using different OTU picking strategies (closed or open reference) to assign reads to a taxonomic group based on their sequence similarities ( Siegwald et al, 2017 ).…”